I'm new on NoSQL Database and i choose MongoDB as my first NoSQL Database. I made an aggregation pipeline to shows the data that i want, here's my document sample:
Document sample from Users Collection
{
"_id": 9,
"name": "Sample Name",
"email": "email#example.com",
"password": "password hash"
}
Document sample from Pages Collection (this one doesn't really matter)
{
"_id": 42,
"name": "Product Name",
"description": "Product Description",
"user_id": 8,
"rating_categories": [{
"_id": 114,
"name": "Build Quality"
}, {
"_id": 115,
"name": "Price"
}, {
"_id": 116,
"name": "Feature"
}, {
"_id": 117,
"name": "Comfort"
}, {
"_id": 118,
"name": "Switch"
}]
}
Document sample from Reviews Collection
{
"_id": 10,
"page_id": 42, #ID reference from pages collection
"user_id": 8, #ID reference from users collection
"review": "The review of the product",
"ratings": [{
"_id": 114, #ID Reference from pages collection of what rating category it is
"rating": 5
}, {
"_id": 115,
"rating":4
}, {
"_id": 116,
"rating": 5
}, {
"_id": 117,
"rating": 3
}, {
"_id": 118,
"rating": 4
}],
"created": "1582825968963", #Date Object
"votes": {
"downvotes": [],
"upvotes": [9] #IDs of users who upvote this review
}
}
I want to get reviews by page_id which can be accessed from the API i made, here's the expected result from the aggregation:
[
{
"_id": 10, #Review of the ID
"created": "Thu, 27 Feb 2020 17:52:48 GMT",
"downvote_count": 0, #Length of votes.downvotes from reviews collection
"page_id": 42, #Page ID
"ratings": [ #Stores what rate at what rating category id
{
"_id": 114,
"rating": 5
},
{
"_id": 115,
"rating": 4
},
{
"_id": 116,
"rating": 5
},
{
"_id": 117,
"rating": 3
},
{
"_id": 118,
"rating": 4
}
],
"review": "The Review",
"upvote_count": 0, #Length of votes.upvotes from reviews collection
"user": { #User who reviewed
"_id": 8, #User ID
"downvote_count": 0, #How many downvotes this user receive from all of the user's reviews
"name": "Sample Name", #Username
"review_count": 1, #How many reviews the user made
"upvote_count": 1 #How many upvotes this user receive from all of the user's reviews
},
"vote_state": 0 #Determining vote state from the user (who requested to the API) for this review, 0 for no vote, -1 for downvote, 1 for upvote
},
...
]
Here's the pipeline of the aggregation for reviews collection that i made for the result above:
user_id = 9
page_id = 42
pipeline = [
{"$group": {
"_id": {"user_id":"$user_id", "page_id": "$page_id"},
"review_id": {"$last": "$_id"},
"page_id": {"$last": "$page_id"},
"user_id" : {"$last": "$user_id"},
"ratings": {"$last": "$ratings"},
"review": {"$last": "$review"},
"created": {"$last": "$created"},
"votes": {"$last": "$votes"},
"upvote_count": {"$sum":
{"$cond": [
{"$ifNull": ["$votes.upvotes", False]},
{"$size": "$votes.upvotes"},
0
]}
},
"downvote_count": {"$sum":
{"$cond": [
{"$ifNull": ["$votes.downvotes", False]},
{"$size": "$votes.downvotes"},
0
]}
}}},
{"$lookup": {
"from": "users",
"localField": "user_id",
"foreignField": "_id",
"as": "user"
}},
{"$unwind": "$user"},
{"$lookup": {
"from": "reviews",
"localField": "user._id",
"foreignField": "user_id",
"as": "user.reviews"
}},
{"$addFields":{
"_id": "$review_id",
"user.review_count": {"$size": "$user.reviews"},
"user.upvote_count": {"$sum":{
"$map":{
"input":"$user.reviews",
"in":{"$cond": [
{"$ifNull": ["$$this.votes.upvotes", False]},
{"$size": "$$this.votes.upvotes"},
0
]}
}
}},
"user.downvote_count": {"$sum":{
"$map":{
"input":"$user.reviews",
"in":{"$cond": [
{"$ifNull": ["$$this.votes.downvotes", False]},
{"$size": "$$this.votes.downvotes"},
0
]}
}
}},
"vote_state": {"$switch": {
"branches": [
{"case": { "$and" : [
{"$ifNull": ["$votes.upvotes", False]},
{"$in": [user_id, "$votes.upvotes"]}
]}, "then": 1
},
{"case": { "$and" : [
{"$ifNull": ["$votes.downvotes", False]},
{"$in": [user_id, "$votes.downvotes"]}
]}, "then": -1
},
],
"default": 0
}},
}},
{"$project":{
"user.password": 0,
"user.email": 0,
"user_id": 0,
"review_id" : 0,
"votes": 0,
"user.reviews": 0
}},
{"$sort": {"created": -1}},
{"$match": {"page_id": page_id}},
]
Note: User can make multiple reviews for same page_id, but only the latest will be shown
I'm using pymongo btw, that's why operators have quotation mark
My questions are:
Is there any room to optimize my aggregation pipeline?
Is it considered as a good practice to have multiple small aggregate execution to get datas like above, or its always better to have 1 big aggregation (or as less as possible) to get the data that i want?
As you can see, every time i want to access votes.upvotes or votes.downvotes from a document on review collection, i checked whether the field is null or not, that's because the field votes.upvotes and votes.downvotes isn't being made when user make a review, instead it's being made when an user gives a vote to that review. Should i make an empty field on votes.upvotes and votes.downvotes when user make a review and remove the $ifNull? Will that increase the performance of the aggregation?
Thanks
Check if this aggregation has better performance.
Create these indexes if you don't have already:
db.reviews.create_index([("page_id", 1)])
Note: We can improve even more the performance avoiding $lookup reviews again.
db.reviews.aggregate([
{
$match: {
page_id: page_id
}
},
{
$addFields: {
request_user_id: user_id
}
},
{
$group: {
_id: {
page_id: "$page_id",
user_id: "$user_id",
request_user_id: "$request_user_id"
},
data: {
$push: "$$ROOT"
}
}
},
{
$lookup: {
"from": "users",
"let": {
root_user_id: "$_id.user_id"
},
"pipeline": [
{
$match: {
$expr: {
$eq: [
"$$root_user_id",
"$_id"
]
}
}
},
{
$lookup: {
"from": "reviews",
"let": {
root_user_id: "$$root_user_id"
},
"pipeline": [
{
$match: {
$expr: {
$eq: [
"$$root_user_id",
"$user_id"
]
}
}
},
{
$project: {
user_id: 1,
downvote_count: {
$size: "$votes.downvotes"
},
upvote_count: {
$size: "$votes.upvotes"
}
}
},
{
$group: {
_id: null,
review_count: {
$sum: {
$cond: [
{
$eq: [
"$$root_user_id",
"$user_id"
]
},
1,
0
]
}
},
upvote_count: {
$sum: "$upvote_count"
},
downvote_count: {
$sum: "$downvote_count"
}
}
},
{
$unset: "_id"
}
],
"as": "stats"
}
},
{
$project: {
tmp: {
$mergeObjects: [
{
_id: "$_id",
name: "$name"
},
{
$arrayElemAt: [
"$stats",
0
]
}
]
}
}
},
{
$replaceWith: "$tmp"
}
],
"as": "user"
}
},
{
$addFields: {
first: {
$mergeObjects: [
"$$ROOT",
{
$arrayElemAt: [
"$data",
0
]
},
{
user: {
$arrayElemAt: [
"$user",
0
]
},
created: {
$toDate: {
$toLong: {
$arrayElemAt: [
"$data.created",
0
]
}
}
},
downvote_count: {
$reduce: {
input: "$data.votes.downvotes",
initialValue: 0,
in: {
$add: [
"$$value",
{
$size: "$$this"
}
]
}
}
},
upvote_count: {
$reduce: {
input: "$data.votes.upvotes",
initialValue: 0,
in: {
$add: [
"$$value",
{
$size: "$$this"
}
]
}
}
},
vote_state: {
$cond: [
{
$gt: [
{
$size: {
$filter: {
input: "$data.votes.upvotes",
cond: {
$in: [
"$_id.request_user_id",
"$$this"
]
}
}
}
},
0
]
},
1,
{
$cond: [
{
$gt: [
{
$size: {
$filter: {
input: "$data.votes.downvotes",
cond: {
$in: [
"$_id.request_user_id",
"$$this"
]
}
}
}
},
0
]
},
-1,
0
]
}
]
}
}
]
}
}
},
{
$unset: [
"first.data",
"first.votes",
"first.user_id",
"first.request_user_id"
]
},
{
$replaceWith: "$first"
},
{
"$sort": {
"created": -1
}
}
])
MongoPlayground
Related
I am currently stuck in this problem, i am relatively new to MongoDB, and i have to retrieve number of reports(count of reports done by users ) for a specific user with his name(name), last reported time(time of last reported post), last reason(report_description) ,
i am stuck here since 2 days now, help will be appreciated .
reported posts collection
{
"created_at": {
"$date": "2021-12-21T18:45:27.489Z"
},
"updated_at": {
"$date": "2021-12-21T18:45:27.489Z"
},
"post_id": {
"$oid": "61955ac35b3475f1d9759255"
},
"user_id": 2,
"report_type": "this is test",
"report_description": "this"
}
Post collection
{
"created_at": {
"$date": "2021-11-17T19:24:53.484Z"
},
"updated_at": {
"$date": "2021-11-17T19:24:53.484Z"
},
"user_id": 8,
"privacy_type": "public",
"post_type": "POST",
"post": "Om Sai Ram",
"total_like": 7,
"total_comment": 0,
"total_share": 0,
"image_url_list": [{
"image_url": "post_images/user-8/a31e39334987463bb9faa964391a935e.jpg",
"image_ratio": "1"
}],
"video_url_list": [],
"tag_list": [],
"is_hidden": false
}
User collection
{
"name": "sathish",
"user_id": 1,
"device_id": "faTOi3aVTjyQnBPFz0L7xm:APA91bHNLE9anWYrKWfwoHgmGWL2BlbWqgiVjU5iy7JooWxu26Atk9yZFxVnNp2OF1IXrXm4I6HdVJPGukEppQjSiUPdMoQ64KbOt78rpctxnYWPWliLrdxc9o1VdKL0DGYwE7Y6hx1H",
"user_name": "sathishkumar",
"updated_at": {
"$date": "2021-11-17T19:13:52.668Z"
},
"profile_picture_url": "1"
}
flask_snip.py
flagged_posts = mb.db_report.aggregate([{
'$group':{
'_id':'$user_id',
}
}])
expected out should be list e.g
[
{
'user_id':1,
'name' :'somename',
'no_of_reports':30,
'last_reported_time':sometime,
'reason':'reason_of lastreported_post',
'post_link':'someurl',
},
{
'user_id':2,
'name' :'somename',
'no_of_reports':30,
'last_reported_time':sometime,
'reason':'reason_of last_reported_post',
'post_link':'someurl',
},
{
'user_id':3,
'name' :'somename',
'no_of_reports':30,
'last_reported_time':sometime,
'reason':'reason_of lastreported_post',
'post_link':'someurl',
},
]
Starting from the reported collection, you can $group to get the last_reason and last_reported_time. Then, perform a $lookup to user collection to get the name.
db.reported.aggregate([
{
"$sort": {
updated_at: -1
}
},
{
"$group": {
"_id": "$user_id",
"last_reported_time": {
"$first": "$updated_at"
},
"last_reason": {
"$first": "$report_description"
},
"no_of_reports": {
$sum: 1
}
}
},
{
"$lookup": {
"from": "user",
"localField": "_id",
"foreignField": "user_id",
"as": "userLookup"
}
},
{
"$unwind": "$userLookup"
},
{
"$project": {
"user_id": "$_id",
"name": "$userLookup.user_name",
"no_of_reports": 1,
"last_reported_time": 1,
"last_reason": 1
}
}
])
Here is the Mongo playground for your reference.
I have a following collection in MongoDB:
{
"_id" : ObjectId("5bbc86e5c16a27f1e1bd39f8"),
"name" : "swetha",
"nameId" : 123,
"source" : "Blore",
"sourceId" : 10,
"LastUpdate" : "10-Oct-2018"
}
{
"_id" : ObjectId("5bbc86e5c16a27f1e1bd39f9"),
"name" : "swetha",
"nameId" : 123,
"source" : "Mlore",
"sourceId" : "11",
"LastUpdate" : "11-Oct-2018"
}
{
"_id" : ObjectId("5bbc86e5c16a27f1e1bd39fa"),
"name" : "swathi",
"nameId" : 124,
"source" : "Mlore",
"sourceId" : "11",
"LastUpdate" : "9-Oct-2018"
}
I am a beginner to Python and want to compare the 'LastUpdate' between the above records based on matching 'name' or 'nameId' and want to push the record with latest date to another collection. E.g. name:'Swetha' is same in first two records. So compare 'LastUpdate' between them and output the record with latest date.
I have written following code to read data records from MongoDB and to print. I didn't understand how to compare records within a same key and compare their timestamp though I referred few resources on Google.
import json
import pandas as pd
from pymongo import MongoClient
try:
client = MongoClient()
print("Connected successfully!!!")
except:
print("Could not connect to MongoDB")
# database
db = client.conn
collection = db.contactReg
df = collection.find()
for row in df:
print(row)
Links that are ref
Is there a better way to compare dictionary values
https://gis.stackexchange.com/questions/87276/how-to-compare-values-from-a-column-in-attribute-table-with-values-in-dictionary
Comparing two dictionaries and printing key value pair in python and few more.
I think what you need is an aggregation. This might look big but once you get the hang out of mongo aggregations you'll get comfortable.
df = collection.aggregate([
{
"$project": {
"_id": 0,
"name": 1,
"nameId": 1,
"source": 1,
"sourceId": 1,
"LastUpdate": 1,
"LastUpdateArray": {
"$split": [
"$LastUpdate",
"-"
]
}
}
},
{
"$project": {
"name": 1,
"nameId": 1,
"source": 1,
"sourceId": 1,
"LastUpdate": 1,
"LastUpdateArray": 1,
"LastUpdateMonth": {
"$arrayElemAt": [
"$LastUpdateArray",
1
]
}
}
},
{
"$project": {
"name": 1,
"nameId": 1,
"source": 1,
"sourceId": 1,
"LastUpdate": 1,
"Year": {
"$arrayElemAt": [
"$LastUpdateArray",
2
]
},
"Date": {
"$arrayElemAt": [
"$LastUpdateArray",
0
]
},
"Month": {
"$switch": {
"branches": [
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Jan"
]
},
"then": "01"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Feb"
]
},
"then": "02"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Mar"
]
},
"then": "03"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Apr"
]
},
"then": "04"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"May"
]
},
"then": "05"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Jun"
]
},
"then": "06"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Jul"
]
},
"then": "07"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Aug"
]
},
"then": "08"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Sep"
]
},
"then": "09"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Oct"
]
},
"then": "10"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Nov"
]
},
"then": "11"
},
{
"case": {
"$eq": [
"$LastUpdateMonth",
"Dec"
]
},
"then": "12"
}
],
"default": "01"
}
}
}
},
{
"$project": {
"name": 1,
"nameId": 1,
"source": 1,
"sourceId": 1,
"LastUpdate": 1,
"Year": 1,
"Date": 1,
"Month": 1,
"DateString": {
"$concat": [
"$Year",
"-",
"$Month",
"-",
"$Date"
]
}
}
},
{
"$project": {
"name": 1,
"nameId": 1,
"source": 1,
"sourceId": 1,
"LastUpdate": 1,
"Date": {
"$dateFromString": {
"dateString": "$DateString"
}
}
}
},
{
"$sort": {
"Date": -1
}
},
{
"$group": {
"_id": "$name",
"name": {
"$first": "$name"
},
"nameId": {
"$first": "$nameId"
},
"source": {
"$first": "$source"
},
"sourceId": {
"$first": "$sourceId"
},
"LastUpdate": {
"$first": "$LastUpdate"
},
"Date": {
"$first": "$Date"
}
}
},
{
"$project": {
"name": 1,
"nameId": 1,
"source": 1,
"sourceId": 1,
"LastUpdate": 1
}
}
])
The first 5 steps of aggregation, I tried to convert it into a date and then sort descending by date. In group by I grouped with name and took the first that comes with that name.
Hope this helps.
I'm assuming what you need is duplicate records and I'm taking the first one that comes. Reference : https://stackoverflow.com/a/26985011/7630071
df = collection.aggregate([
{
"$group": {
"_id": "$name",
"count": {
"$sum": 1
},
"data": {
"$push": {
"nameId": "$nameId",
"source": "$source",
"sourceId": "$sourceId",
"LastUpdate": "$LastUpdate"
}
}
}
},
{
"$match": {
"_id": {
"$ne": null
},
"count": {
"$gt": 1
}
}
}
])
There are 18 records under a company but I can see only 3 records from them. Below is my query and the python code.
{
"query": {
"nested": {
"inner_hits": {
"_source": [
"name",
"country",
"_matched_experiences.role"
]
},
"path": "socials",
"query": {
"match": { "socials._has_email": "true"
}
}
}
},
"_source": [
"com_name"
]
}
Below is my python code.
with open(OUTPUT_FILENAME_1, "a") as f1:
csv_writer_1 = csv.writer(f1)
csv_writer_1.writerow(["company_name","name","country","role"])
query_dictionary = {above query}
scroll = elasticsearch.helpers.scan(es, query=query_dictionary, index=companydirectory, scroll='60m', size=800)
for res in scroll:
try:
record_fields = res["_source"]
name = ""
com_name = ""
company_name = record_fields.get("com_name") #from the ES
name_record_fields = res["inner_hits"]["social_contacts"]["hits"]["hits"]
for j in name_record_fields:
name = j['_source']['name'] #from ES
k = j['_source']['_matched_experiences']
role = k[0].get('role')
country = j['_source']['country']
print company_name,name,validated_email_fromES, function_id,level_id, country, company_name,role
# csv_writer_1.writerow([company_name.encode('utf8'),name.encode('utf8'),country,role.encode('utf8')])
except Exception as e1:
pass
This is the sample output from ES :
"_source": {
"company_name": "Rothborns"
},
"inner_hits": {
"social_contacts": {
"hits": {
"total": 18,
"max_score": 9.87977,
"hits": [
{
"_type": "comp_directory",
"_id": "MC9MY",
"_nested": {
"field": "socials",
"offset": 36
},
"_score": 9.787,
"_source": {
"country": "SA",
"name": "warner Pauli",
"_has_email": true,
"_matched_experiences": [
{
"role": "Financial Controller"
}
]
}
The total record is 18 in Rothborns. But I can get only 3 records from Rothborns in the output file.
Kindly help. Thanks.
The reason is because when using inner_hits the size is 3 by default. You simply need to change your query to this:
{
"query": {
"nested": {
"inner_hits": {
"size": 100, <--- add this
"_source": [
"name",
"country",
"_matched_experiences.role"
]
},
"path": "socials",
"query": {
"match": {
"socials._has_email": "true"
}
}
}
},
"_source": [
"com_name"
]
}
test_cursor = db.command({
"aggregate": "New_layout",
"pipeline": [
{ "$match": { "$and": [
{ "FIRST_DATE": { "$gte": new_date } },
{ "CHAIN_ID": { "$ne": "" } }
] } },
{ "$unwind": { "path": "$ENTERS", "includeArrayIndex": "Date" } },
{ "$project": {
"_id": 0,
"SITE_ID": "$SITE_ID",
"CHAIN_ID": "$CHAIN_ID",
"SEGMENT_ID": "$SEGMENT_ID",
"ZIP": "$ZIP",
"ZIP3": "$ZIP3",
"MARKET_ID": "$MARKET_ID",
"REGION": "$REGION",
"MALL_CODE": "$MALL_CODE",
"MALL_AREA": "$MALL_AREA",
"MALL_NAME": "$MALL_NAME",
"FIRST_DATE": "$FIRST_DATE",
"MARKET_AREA": "$MARKET_AREA",
"REGION_AREA": "$REGION_AREA",
"ZIP_AREA": "$ZIP_AREA",
"ZIP3_AREA": "$ZIP3_AREA",
"DATE": "$Date",
"ENTERS": "$ENTERS"
} }
],
"allowDiskUse": bool(1),
"cursor": {}
})
asd=list(test_cursor)
The contents of the cursor are as below :-
[u'cursor', u'ok', u'waitedMS'] .
However with an $out statement, the output collection has the expected contents.
I am running pymongo v3.2.2 and mongo 3.2. I was told this problem is experienced with v3.0 or lesser, but this is something I am not able to figure out
You should use aggregate() instead of command().
test_cursor = db.New_layout.aggregate([
{ "$match": { "$and": [
{ "FIRST_DATE": { "$gte": new_date } },
{ "CHAIN_ID": { "$ne": "" } }
] } },
{ "$unwind": { "path": "$ENTERS", "includeArrayIndex": "Date" } },
{ "$project": {
"_id": 0,
"SITE_ID": "$SITE_ID",
"CHAIN_ID": "$CHAIN_ID",
"SEGMENT_ID": "$SEGMENT_ID",
"ZIP": "$ZIP",
"ZIP3": "$ZIP3",
"MARKET_ID": "$MARKET_ID",
"REGION": "$REGION",
"MALL_CODE": "$MALL_CODE",
"MALL_AREA": "$MALL_AREA",
"MALL_NAME": "$MALL_NAME",
"FIRST_DATE": "$FIRST_DATE",
"MARKET_AREA": "$MARKET_AREA",
"REGION_AREA": "$REGION_AREA",
"ZIP_AREA": "$ZIP_AREA",
"ZIP3_AREA": "$ZIP3_AREA",
"DATE": "$Date",
"ENTERS": "$ENTERS"
} }
],
allowDiskUse=True)
I have 'device' type documents, which I search by model using following query (using Flask & Elasticsearch as an api):
match handset
query = {
"query": {
"match_phrase": {
"model": model_name
}
},
"track_scores": True,
"size": 1,
"sort":
[
{"_score": {"order": "desc"}},
{"model": {"order": "asc"}}
]
}
device = es.search(body=query, doc_type='device')
That returns single device with 'model' closest to requested (model_name).
Example list of devices:
[{ "id":482,
"memory":"16",
"model":"iPhone 5s 16GB" },
{ "id":483,
"memory":"32",
"model":"iPhone 5s 32GB" },
{ "id":484,
"memory":"16",
"model":"iPhone 5c 16GB" },
{ "id":486,
"memory":"64",
"model":"iPhone 6 64GB" },
{ "id":485,
"memory":"32",
"model":"iPhone 6 32GB" }]
How can I change it so it return device with the lowest memory?
>>> query.query.match_phrase.model = 'iPhone 5s'
>>> device = es.search(body=query, doc_type='device')
{ "id":482,
"memory":"16",
"model":"iPhone 5s 16GB" }
>>> query.query.match_phrase.model = 'iPhone 6'
>>> device = es.search(body=query, doc_type='device')
{ "id":485,
"memory":"32",
"model":"iPhone 6 32GB" }
Any clues highly appreciated.
I would change the type of the "memory" field to "integer" in your mapping, and index the data appropriately, then it's easy to get the result you want.
So, with a mapping like this:
PUT /test_index
{
"mappings": {
"doc": {
"_id": {
"path": "id"
},
"properties": {
"id": {
"type": "integer"
},
"memory": {
"type": "integer"
},
"model": {
"type": "string"
}
}
}
}
}
and these documents indexed:
POST /test_index/doc/_bulk
{"index":{}}
{"id":482,"memory":16,"model":"iPhone 5s 16GB"}
{"index":{}}
{"id":483,"memory":32,"model":"iPhone 5s 32GB"}
{"index":{"_id":1}}
{"id":484,"memory":16,"model":"iPhone 5c 16GB"}
{"index":{}}
{"id":486,"memory":64,"model":"iPhone 6 64GB"}
{"index":{}}
{"id":485,"memory":32,"model":"iPhone 6 32GB"}
{"index":{}}
You can query like this to get the lowest memory hit on "iPhone 5s":
POST /test_index/_search
{
"query": {
"match": {
"model": {
"query": "iPhone 5s",
"operator": "and"
}
}
},
"sort": [
{
"memory": {
"order": "asc"
}
}
],
"size": 1
}
...
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 2,
"max_score": null,
"hits": [
{
"_index": "test_index",
"_type": "doc",
"_id": "482",
"_score": null,
"_source": {
"id": 482,
"memory": 16,
"model": "iPhone 5s 16GB"
},
"sort": [
16
]
}
]
}
}
Here's the code I used:
http://sense.qbox.io/gist/8441d7379485e03a75fdbaa9ae0bf9748098be33